ArticleRadiology2018
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs.
Article in Radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 199 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
199 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.Emergency radiology · 2025Pooled it
- Application of convolutional network models in detection of intracranial aneurysms: A systematic review and meta-analysis.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2023Pooled it
- Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.PloS one · 2019Pooled it
- Artificial intelligence algorithm improves radiologists' bone age assessment accuracy.Journal of the Chinese Medical Association : JCMA · 2025Trial
- Dual-input deep learning bone age assessment in late adolescence using hand and knee radiographs.Skeletal radiology · 2026Article
- Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation.Scientific reports · 2026Article
- Quantifying human variability in pediatric bone age assessment: a multi-institutional multi-reader study and benchmark for AI evaluation.Pediatric radiology · 2026Article
- Adult Age Threshold Estimation Using Radiographic Evaluation of Wrist-Hand Skeletal Maturation: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- A two-stage deep learning approach for forensic age and sex determination from hand-wrist radiographs: an anatomical pose estimation and multi-task model.International journal of legal medicine · 2026Article
- MedFusionT5: Cross-Modal Attention Boosts Semantic Quality and Reduces Hallucinations in Dental AI.International dental journal · 2026Article
- A two-stream network with global-local feature fusion for bone age assessment.Scientific reports · 2026Article
- Integrating Biological Maturity into Fitness Assessment and Physical Activity Interventions in Children and Adolescents: A Narrative Review.Sports (Basel, Switzerland) · 2026Review
- [Artificial intelligence algorithm for bone age estimation].Revista medica del Instituto Mexicano del Seguro Social · 2026Observational
- Bibliometric Analysis of Artificial Intelligence in Pediatric Radiology and Medical Imaging: A Focus on Deep Learning Applications.Bioengineering (Basel, Switzerland) · 2026Review
- Developing new methods for the accurate evaluation of children's bone age based on X-ray images: a study on the accuracy and consistency of artificial intelligence and manual bone age evaluation.Quantitative imaging in medicine and surgery · 2026Article
- A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.Journal of imaging informatics in medicine · 2026Article
- Atlas-Assisted Bone Age Estimation from Hand-Wrist Radiographs Using Multimodal Large Language Models: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Automatic bone age assessment: a deep learning case study on the Brazilian population with a supporting mobile application prototype.Scientific reports · 2026Article
- digiBONE: an automated tool for segmental Greulich-Pyle bone age assessment of Indian children and adolescents.Frontiers in endocrinology · 2026Article
- Final height prediction of girls at menarche: a combined model using left hand and wrist bone age, knee radiomic scores, and clinical characteristics.World journal of pediatrics : WJP · 2026Article
139 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To compare the performance of a deep-learning bone age assessment model based on hand radiographs with that of expert radiologists and that of existing automated models. Materials and Methods The institutional review board approved the study. A total of 14 036 clinical hand radiographs and corresponding reports were obtained from two children's hospitals to train and validate the model. For the first test set, composed of 200 examinations, the mean of bone age estimates from the clinical report and three additional human reviewers was used as the reference standard. Overall model performance was assessed by comparing the root mean square (RMS) and mean absolute difference (MAD) between the model estimates and the reference standard bone ages. Ninety-five percent limits of agreement were calculated in a pairwise fashion for all reviewers and the model. The RMS of a second test set composed of 913 examinations from the publicly available Digital Hand Atlas was compared with published reports of an existing automated model. Results The mean difference between bone age estimates of the model and of the reviewers was 0 years, with a mean RMS and MAD of 0.63 and 0.50 years, respectively. The estimates of the model, the clinical report, and the three reviewers were within the 95% limits of agreement. RMS for the Digital Hand Atlas data set was 0.73 years, compared with 0.61 years of a previously reported model. Conclusion A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of existing automated models.
Indexed as
Identifiers
29095675What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.